DETAILED ACTION
1. Claims 1-4 and 7 are pending in this application.
Notice of Pre-AIA or AIA Status
2. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. §102 and §103 (or as subject to pre-AIA 35 U.S.C. §102 and §103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Continued Examination Under 37 CFR 1.114
3. A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/28/2026 has been entered.
Response to Amendment
4. This office action is in response to applicant’s amendment filed on 05/28/2026 in response to the final rejection mailed on 03/18/2026. Claim 1 have been amended. Claims 2-4 and 7 have been kept original. Amendment has been entered.
Response to Arguments
5. Applicant's arguments, filed on 05/28/2026, with respect to the rejection of claims 1-4 and 7 under 35 U.S.C. §101 an abstract idea (mental process) (Applicant’s arguments, pages 5-9), have been fully considered but are not persuasive. Respectfully, the examiner disagrees, see the clarification below.
A cluster label model (or Mi cluster label model) is a post-processing technique in unsupervised learning that assigns meaningful, human-readable labels to clusters produced by clustering algorithms. Its main goal is to make the resulting groupings interpretable so that users can understand what each cluster represents, especially in applications like document clustering, image segmentation, or customer segmentation. A human can mentally assign labels to data and form clusters and subclusters. A human can mentally assign labels to data and form clusters and subclusters, for example, by grouping similar customer reviews into broad categories like “positive” and “negative,” and then further subdividing each into subclusters such as “product quality” and “customer service” based on recurring themes.
The limitation that simply predicts results from cluster data using basic mathematical relationships is not statutory under 35 U.S.C. § 101. The limitation claimed simply applies basic mathematical relationships (e.g., clustering, regression, correlation, optimization algorithms) to data.
The idea of unifying or organizing elements of a cluster is a simple task that can be accomplished in the human mind. A human can observe clustered data elements, judge them, and mentally organize them into a different cluster or even produce another cluster. For example, a human can glance at scattered shapes, mentally group them by color, and instantly reorganize them into a new pattern. The claim does not show any element that makes this task difficult or anything that would not be comfortable to produce in the human mind. The specification also lacks details linking any of the claim elements to explain the complexity of performing the claim steps.
The additional elements presented in the amendments broadly describe how mental processes and mathematical relationships are implemented. The additional elements presented in the amendments do not advance any technological field. Therefore, the additional elements presented in the amendments do not integrate the claim into a practical application.
For this reason, the § 101 abstract idea (mental process) rejection of claims 1-4 and 7 is upheld.
Allowable Subject Matter
6. Claims 1-4 and 7 are allowed over prior art. However, the claims are not statutory and are rejected under 35 U.S.C. § 101 for being directed to an abstract idea (mental processes and/or mathematical relationships).
Claim Rejections - 35 USC § 101
7. 35 U.S.C. §101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-4 and 7 are rejected under 35 U.S.C. §101 because the claimed invention is directed to an abstract idea (mental processes and/or mathematical relationships) without significantly more. The claims describe steps to evaluate cluster stability indicators.
The following is an analysis based on 2019 Revised Patent Subject Matter Eligibility Guidance (2019 PEG).
Step 1, Statutory Category?
Claims 1-4 and 7 are directed to a method.
Therefore, claims 1-4, and 7 fall into at least one of the four statutory categories.
Step 2A, Prong I: Judicial Exception Recited?
The examiner submits that the foregoing claim limitations constitute a “Mental Process”, as the claims cover performance of the limitations in the human mind, given the broadest reasonable interpretation.
As per independent claim 1, the claim recites the limitations of:
“uniformly down-sampling a raw data to be clustered to generate a plurality of groups of sub-data thereof;” A human can observe and mentally judge data using criteria that would create a group of the data for each criterion. For example, imagine a human recruiter receives 10 resumes for a job opening. They need to quickly sort these into different groups for further review. The recruiter mentally applies several specific criteria to create these groups. There is nothing so complex in the limitation that could not be doing in the human mind.
“calculating a plurality of similarities of the raw data to be clustered with the plurality of groups of sub-data according to at least one statistical test;” A human can observe data and judge the observed data using statistical criteria to identify similarity between them. Upon identify the similarity a human can mentally group the data in groups. The statistical test is merely an element used to implement the abstract idea herein. For example, imagine a real estate agent assesses house values on a street by mentally scanning properties. The agent identifies houses fitting their "mental baseline" ("similar homes") versus those that do not ("outliers"). There is nothing so complex in the limitation that could not be doing in the human mind.
“keeping the plurality of groups of sub-data with the plurality of similarities greater than a similarity threshold as a plurality of groups of the sub-data to be analyzed;” A human can observe a collection of data and apply a threshold to the observed data to identify data in the collection that is above the applied threshold. For example, imagine a person checking canned goods sets an expiration date threshold (e.g., November 1, 2025). They observe each can's date and apply the threshold, removing any identified as expired (before the threshold date). There is nothing so complex in the limitation that could not be doing in the human mind.
“clustering the plurality of groups of the sub-data to be analyzed according to a cluster algorithm to generate a plurality of sub-data cluster results;” A human can observe a collection of data and make judgments according with the criteria to generate a sub-collection of the observed data. For example, imagine a photographer has just finished a wedding shoot and needs to select the best photos to edit and deliver to the client. The raw data is a collection of 1,500 unedited digital images, and the selected best photos are the result of the judgments of the raw data, thereby creating the sub-collection of the observed data. The cluster algorithm is merely an element used to implement the abstract idea herein. There is nothing so complex in the limitation that could not be doing in the human mind.
“organizing a plurality of cluster label models of the sub-data cluster results and generating a plurality of organized sub-data cluster results according to the organized cluster label models;” A human can observe data that represent models and mentally organize observed models. For example, imagine a city planner reviews blueprints (data representing models) for a development project. The planner applies mental criteria (like cost or sustainability) to mentally sort and organize these distinct architectural models into an order or groups for a meeting. There is nothing so complex in the limitation that could not be doing in the human mind.
“calculating a cluster stability indicator according to the organized sub-data cluster results.” A human can mentally observe data that is organized in a cluster and define indicators of stability based on simple criteria pre-established. For example, a data analyst observes customer location clusters on a map. The analyst applies pre-established visual criteria (density, separation, size consistency) to mentally judge the stability of these distinct groupings. There is nothing so complex in the limitation that could not be doing in the human mind.
“generating the organized sub-data cluster results according to the organized cluster label models comprises the following sub-step:” A human can mentally observe and organize data in group. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing a decision tree classifier to overfit a model prediction for the plurality of cluster label models of the plurality of sub-data cluster results to generate the organized sub-data cluster results;” A human can mentally observe, judge and organize data in group and subgroups. There is nothing so complex in the limitation that could not be doing in the human mind.
“building a Mi cluster label model of the raw data to be clustered;” A human can observe data in a cluster and assign cluster labels to data points. There is nothing so complex in the limitation that could not be doing in the human mind.
“building a Mii cluster label model of the ith group of the sub-data;” A human can observe data in a sub-cluster and assign cluster labels to data points. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing the Mi cluster label model to predict the ith group of the sub-data to generate a i_i cluster result;” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data.
A human can analyze cluster data, evaluate it, and generate a result based on that mental assessment. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing the Mii cluster label model to predict the ith group of the sub-data to generate a ii_i cluster result:” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data.
A human can analyze sub-cluster data, evaluate it, and generate a result based on that mental assessment. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing the Mi cluster label model to predict the raw data to be clustered to generate a i_ii cluster result:” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data.
A human can analyze sub-cluster data, evaluate it, and generate a result based on that mental assessment. There is nothing so complex in the limitation that could not be doing in the human mind.
“utilizing the trained ith decision tree 1 to predict the i_ii cluster result for converting the i_ii cluster result to a i_ii_C cluster result to be the ith organized sub-data cluster result;” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data and produce a result in the limitation.
A human can analyze sub-cluster data, evaluate it, and generate a result based on that mental assessment. There is nothing so complex in the limitation that could not be doing in the human mind.
“wherein the step for calculating the cluster stability indicator according to the organized sub-data cluster results comprises the following sub-steps:” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data and produce a result in the limitation.
“calculating a plurality of cluster probabilities of a plurality of cluster labels of a plurality of data points in the raw data to be clustered according to the organized sub-data cluster results;” Calculating predictions is done using mathematical relationships. There is nothing so complex in utilizing mathematical relationships to calculate predictions for a group of data and produce a result in the limitation.
A human can analyze sub-cluster data, evaluate it, and generate a result based on that mental assessment. There is nothing so complex in the limitation that could not be doing in the human mind.
“averaging a plurality of highest cluster probabilities of each of the plurality of data points in the raw data to be clustered to generate the cluster stability indicator.” Calculating averages is done using mathematical relationships. There is nothing complex about using these formulas to find averages for a data set and produce a result within those limits.
A human can analyze cluster data, evaluate it, and generate indicators mentally. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 2, the claim recites the limitation of:
“preprocessing the raw data to generate the raw data to be clustered.” A human can observe and mentally make judgments about the observed data and organize it based on the result of the judgments into groups. There is nothing so complex in the limitation that could not be doing in the human mind.
As per dependent claim 3, the claim recites the limitation of:
“wherein the raw data comprises at least a numerical feature or a character feature;” The at least one numerical feature or a character feature is merely an element used to implement abstract ideas.
“determining whether the raw data comprises the character feature;” A human observes data and makes a judgment to define if a certain character is in the observed data or not. There is nothing so complex in the limitation that could not be doing in the human mind.
“when the raw data comprises the character feature, converting the character feature in the raw data to a transformation numerical feature to generate the raw data to be clustered;” A human can observe data that has a particular character and mentally transform the particular character into a numerical feature to organize the observed data. For example, a driver mentally assigns numerical values (e.g., Green=1, Red=3) to the observed text or color of a traffic signal to quickly organize their response and make a decision. There is nothing so complex in the limitation that could not be doing in the human mind.
“when the raw data excludes the character feature, utilizing the raw data as the raw data to be clustered.” The when the raw data excludes the character feature, utilizing the raw data as the raw data to be clustered is merely an instruction used to implement abstract ideas.
As per dependent claim 4, the claim recites the limitation of:
“wherein the raw data comprises at least a numerical feature or a character feature;” The at least one numerical feature or a character feature is merely an element used to implement abstract ideas.
“determining whether the raw data comprises the character feature;” A human observes data and makes a judgment to define if a certain character is in the observed data or not. There is nothing so complex in the limitation that could not be doing in the human mind.
“when the raw data comprises the character feature, converting the character feature in the raw data to a transformation numerical feature and standardizing the transformation numerical feature to generate the raw data to be clustered;” A human can observe data that has a particular character and mentally transform the particular character into a numerical feature to organize the observed data. The human can follow a predefine standard to do the transformation. A human can observe data that has a particular character and mentally transform the particular character into a numerical feature to organize the observed data; the human can follow a predefined standard to do this transformation. A simple example is a teacher using a predefined answer key to mentally transform letter grades (A, B, C, D) into numerical points (1 or 0) to organize and score a test. There is nothing so complex in the limitation that could not be doing in the human mind.
“when the raw data excludes the character feature, standardizing the numerical feature of the raw data to generate the raw data to be clustered.” The when the raw data excludes the character feature, standardizing the numerical feature of the raw data to generate the raw data to be clustered. is merely an instruction used to implement abstract ideas.
As per dependent claim 7, the claim recites the limitation of:
“wherein the step for clustering the plurality of groups of the sub-data to be analyzed according to a cluster algorithm to generate a plurality of the sub-data cluster results further comprises the following sub-step:” A human can observe a collection of data and make judgments according with the criteria to generate a sub-collection of the observed data. For example, imagine a photographer has just finished a wedding shoot and needs to select the best photos to edit and deliver to the client. The raw data is a collection of 1,500 unedited digital images, and the selected best photos are the result of the judgments of the raw data, thereby creating the sub-collection of the observed data. The cluster algorithm is merely an element used to implement the abstract idea herein. There is nothing so complex in the limitation that could not be doing in the human mind.
“clustering the raw data to be clustered according to the cluster algorithm to generate a raw data cluster result;” A human can observe data, make judgments, and based on the judgments, organize data into groups. The cluster algorithm is merely an element used to implement the abstract idea herein. There is nothing so complex in the limitation that could not be doing in the human mind.
“wherein the indicator evaluation method of the cluster stability further comprises the following sub-step: generating a final cluster result according to the raw data cluster results and the organized sub-data cluster results;” A human can observe data, make judgments, and based on the judgments, organize data into groups; the organized data can be a result or final result judged by the human mentally. A simple example of this is a chef sorting ingredients: they observe various raw foods, judge them mentally (e.g., "this goes in the fridge, this is a vegetable"), and organize them into final groups like the "refrigerator" pile and the "pantry" pile. There is nothing so complex in the limitation that could not be doing in the human mind.
“wherein the final cluster result corresponds to the cluster stability indicator.” The final cluster result corresponding to the cluster stability indicator is merely an instruction used to implement the abstract ideas.
Accordingly, claims 1-4 and 7 recite at least one abstract idea.
Step 2A, Prong II: Integrated into a Practical Application?
The claims recite the following additional limitations/elements:
As per independent claim 1, the claim recites the limitations/elements of:
“a decision tree classifier; a model prediction; wherein the higher the cluster stability indicator is, the higher final cluster result is; wherein the step for organizing the cluster label models of the sub-data cluster results; according to a raw data cluster label model of the raw data cluster results; wherein the 1th cluster label model of the sub-data cluster result is organized by the following steps: and wherein the step for calculating the cluster stability indicator according to the organized sub-data cluster results comprises the following sub-steps:” These are merely limitations/elements used in the implementation of abstract ideas, see MPEP § 2106.05(f).
“using the i_i cluster result as an input training data of the ith decision tree and using the ii_i cluster result as an output training data of the ith decision tree for training the ith decision tree;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis. It just broadly describes using input training data to generate an output training data.
As per dependent claim 2, the claim recites the limitation of:
“receiving a raw data;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
As per dependent claim 2, the claim recites the limitations of:
“storing a raw data;” This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere data gathering, without any further processing or analysis.
“a data storage device” This element is example of mere instruction to implement an abstract idea on a computer, or merely using a computer as a tool to perform an abstract idea (see MPEP § 2106.05(f)). Specifically, the additional elements of the limitations invoke computers or other machinery merely as a tool to perform an existing process. Use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) or simply adding a general-purpose computer or computer components after the fact to an abstract idea (e.g., a fundamental economic practice or mathematical equation) do not provide improvements to the functioning of a computer or to any other technology or technical field; and do not integrate a judicial exception into a practical application.
“wherein the processor is communicatively connected to the data storage device to access the raw data and preprocesses the raw data to generate the raw data to be clustered.” Considering the “communicatively connected to the data storage device to access the raw data” this part of the limitation is that this is a simple example of transmitting data. This limitation is example of adding insignificant extra-solution activity to the judicial exception (see MPEP § 2106.05(g)). Specifically, the additional limitation exemplifies mere transmitting data, without any further processing or analysis.
Therefore, claims 1-4 and 7 do not integrate the recited abstract ideas into a practical application.
Step 2B: Claim provides an Inventive Concept?
With respect to the limitations identified as insignificant extra-solution activity above the conclusions are carried over, and both the “receiving ….; store …; outputting; and communicatively connected to the data storage device to access the raw data” are well-understood, routine, and conventional operations.
For support as being well-understood, routine, and conventional for “receiving ….; store …; inputting …; outputting …; and communicatively connected to the data storage device to access the raw data” as noted by the courts is well understood routine and conventional, see MPEP 2106.05(d)(ii) “i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);” and/or MPEP 2106.05(d)(ii) “iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93;”, and/or MPEP 2106.05(d)(II) “iii. Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log);”.
Looking at the limitations in combination and the claim as a whole does not change this conclusion and the claim is ineligible.
Therefore, the claims 1-4 and 7 are not patent eligible.
Prior Art of Record
8. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Gopalan et al. (US 20070174268 A1), teaches clustering an object.
Gatto et al. (Gatto at el., Label Cluster Chains for Multi-Label Classification, 2024, Department of Computer Science, Federal University of Sao Carlos, Sao Carlos, 13565-905, SP, Brazil. (Year: 2024)), teaches multi-label classification is a type of supervised machine learning that can simultaneously assign multiple labels to an instance.
Conclusion
9. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANTONIO CAIA DO whose telephone number is (469)295-9251. The examiner can normally be reached on Monday - Friday / 06:30 to 16:30.
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/ANTONIO J CAIA DO/
Examiner, Art Unit 2164
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